tracerazor-langgraph 0.1.0

TraceRazor LangGraph/LangChain callback — token efficiency auditing

tracerazor-langgraph

LangGraph/LangChain callback adapter for TraceRazor.

Automatically captures every LLM call and tool call from your LangGraph graph with zero manual instrumentation.

Install

pip install tracerazor-langgraph
pip install tracerazor-langgraph[langgraph]  # includes langgraph

Requires the tracerazor binary:

cargo build --release
export TRACERAZOR_BIN=/path/to/TraceRazor/target/release/tracerazor

Usage

from tracerazor_langgraph import TraceRazorCallback
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

callback = TraceRazorCallback(
    agent_name="support-agent",
    threshold=70,
)

model = ChatOpenAI(model="gpt-4o-mini")
agent = create_react_agent(model, tools=[...])

result = agent.invoke(
    {"messages": [HumanMessage(content="I want a refund")]},
    config={"callbacks": [callback]},
)

# After the agent finishes:
report = callback.analyse()
print(report.markdown())

# CI/CD gate — raises AssertionError if TAS < threshold:
callback.assert_passes()

API

TraceRazorCallback(agent_name, framework, threshold, task_value_score, tracerazor_bin)

param	default	description
agent_name	"langgraph-agent"	shown in all reports
framework	"langgraph"	framework label
threshold	70.0	minimum TAS for assert_passes()
task_value_score	1.0	answer quality (0–1)
tracerazor_bin	auto	path to binary; falls back to TRACERAZOR_BIN env var

callback.analyse() → TraceRazorReport

Finalise and submit the trace. Returns the report.

callback.assert_passes()

Raise AssertionError if TAS < threshold.

callback.set_task_value_score(score: float)

Update quality score before calling analyse().